integrates evaluation workflows with observability, allowing teams to define quality criteria, run automated or human-assisted tests, and compare models or prompts systematically to guide iterative improvements.

Braintrust includes tools like a purpose-built database for storing and querying AI logs and traces, SDKs for multiple programming languages, and an automation agent that assists with tasks such as prompt optimization and dataset generation based on trace data. The platform offers customizable dashboards, alerting mechanisms, and integration with existing development workflows so teams can address regressions and adjust deployments based on real usage insights. Braintrust is designed to work with a wide range of AI frameworks and infrastructure components, enabling consistent quality checks and performance analysis throughout the lifecycle of AI features.